Conditional Information Bottleneck Approach for Time Series Imputation
Authors: MinGyu Choi, Changhee Lee
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments, conducted on multiple real-world datasets, consistently demonstrate that our method significantly improves imputation performance (including both interpolation and extrapolation), and also enhances prediction performance based on the imputed values. |
| Researcher Affiliation | Academia | Min Gyu Choi Massachusetts Institute of Technology, USA chemgyu@mit.edu Changhee Lee Chung-Ang University, Korea changheelee@cau.ac.kr |
| Pseudocode | Yes | We provide the pseudo-algorithm of Time CIB as below: Algorithm 1 Conditional Information Bottleneck on Time Series |
| Open Source Code | Yes | CODE AVAILABILITY Codebase used in this paper is available at https://github.com/Chemgyu/Time CIB. |
| Open Datasets | Yes | Healing MNIST (Krishnan et al., 2015) has approximately 60% of missing pixels under a missingnot-at-random (MNAR) pattern on every time step... Rotated MNIST (Ramchandran et al., 2021) evaluates performance on interpolation and extrapolation... Beijing Air Quality (Zhang et al., 2017) and US Local5 https://www.ncei.noaa.gov/data/local-climatological-data/ whose time series measurements are collected every hour... Physionet2012 Mortality Prediction Challenge (Silva et al., 2012) |
| Dataset Splits | Yes | Table D1: Data Statistics. # Samples Len (T ) Feature Dim # Classes Missing Ratio (ori/art) Healing MNIST 50000/10000/10000 10 28 28 1 10 /60% Rotated MNIST 50000/10000/10000 10 28 28 1 10 /60% |
| Hardware Specification | Yes | All experiments were conducted using a 48GB NVIDIA RTX A6000. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers, such as Python, PyTorch, or TensorFlow versions, only general implementation details. |
| Experiment Setup | Yes | Table C1: Hyperparameter specifications. Hidden Dim Batch Size Epochs Learning Rate Temperature Kernel parameter Healing MNIST 128 64 30 1e 3 1.0 2.0 Rotated MNIST 128 64 30 1e 3 1.0 2.0 Beijing 128 64 100 1e 3 1.0 4.0 US Local 64 16 20 1e 4 1.0 2.0 Physionet2012 16 256 50 1e 3 1.0 32 |